Genetic variants in Chinese patients with sporadic Stanford type A aortic dissection.

BACKGROUND: Genetic disorders are strongly associated with aortic disease. However, the identities of genetic mutations in sporadic Stanford type A aortic dissection (STAAD) are not clear. The present study analysed the possible genetic mutations of the known pathogenic genes of aortic disease and the clinical characteristics in patients with sporadic STAAD.
METHODS: We analysed genetic mutations in 26 genes that underlie aortic aneurysms and dissections in 100 sporadic STAAD patients and 568 healthy controls after whole-genome sequencing (WGS). Clinical features and in-hospital death were determined in all STAAD patients.
RESULTS: In total, 60 suspicious pathogenic mutations (56 novel and 4 previously reported) in 19 genes were identified in 50% (50/100) of patients, and 14 patients had more than 1 mutation. The ascending aortic diameter was extended in patients with mutations (49.1±12.3 vs. 43.7±11.2 mm, P=0.023), and the DeBakey type I phenotype was more common in patients with mutations in genes that coded extracellular matrix (ECM) components than in patients with mutations in other genes (96.6% vs. 66.7%, P=0.007). Patients with fibrillin-1 (FBN1) mutations were younger than patients without FBN1 mutations (44.7±11.0 vs. 53.5±12.1, P=0.030). Subgroup analyses revealed an increased risk of in-hospital mortality in mutation carriers (44.4% vs. 10.5%, P=0.029) but only in patients who received conservative treatment.
CONCLUSIONS: Half of Chinese patients with a sporadic form of STAAD may carry mutations in known pathogenic genes of aortic disease, and these patients may exhibit distinct clinical features and poor clinical outcomes with the use of conservative treatment. 2021 Journal of Thoracic Disease. All rights reserved.

Introduction

Aortic dissection (AD) is a catastrophic cardiovascular condition that involves separation of the layers of the aortic wall. Stanford type A AD (STAAD) involves the ascending aorta, and it is further divided into DeBakey type I or type II based on the extent of the dissection (1,2). Patients with STAAD have a stronger genetic component than patients with Stanford type B AD or abdominal aortic aneurysms, which are more associated with lifestyle-linked risk factors, such as hypertension, atherosclerosis, age and sex (3,4).

Genetic disorders are strongly associated with aortic disease, and genetic disorder-induced aortic wall weakness may cause aortic aneurysms and dissection. Approximately 11–19% of patients with AD have first-degree relatives who are diagnosed with aortic aneurysm or dissection, and 80% of patients show a sporadic form of AD (5,6). Patients with aortic aneurysms and dissections are further classified as nonsyndromic or syndromic according to whether the abnormalities are limited to the cardiovascular system. Hereditary diseases of connective tissues are often associated with syndromic AD, and most of these patients develop an autosomal dominant disorder caused by mutations in certain genes, such as fibrillin-1 [(FBN1); Marfan syndrome (MFS)], fibrillin-2 [(FBN2); Beals syndrome], collagen type III alpha 1 chain [(COL3A1); Ehlers-Danlos syndrome (EDS), vascular type] and transforming growth factor beta receptor [(TGFBR); Loeys-Dietz syndrome (LDS)], whereas mutations located in acetyl coA acetyltransferase 2 (ACAT2), myosin heavy chain 11 (MYH11) and SMAD2 seem to affect patients with a family history and exhibit a nonsyndromic form (7). However, the genetic risk factors for patients with sporadic STAAD are not clear.

Next-generation sequencing (NGS) technology is widely used for clinical testing in the search for a genetic cause of disease. Panel testing of multiple genes has emerged as the preferred approach. However, the method of panel testing presupposes that the abnormalities that are of clinical relevance are confined to the panel of tested genes. Therefore, NGS-based whole-genome sequencing (WGS) and targeted gene panel analysis in combination with complementary methods provide a comprehensive and feasible approach for genetic diagnostics.

We analysed 26 specific genes that are known to underlie aortic aneurysm and dissection in 100 Chinese patients with sporadic STAAD and used WGS to clarify whether genetic variants in suspicious pathogenic genes were associated with sporadic STAAD.

We present the following article in accordance with the STROBE reporting checklist (available at https://dx.doi.org/10.21037/jtd-20-2758).

Methods

Study population and data collection

Patients with suspected STAAD who were admitted to the emergency centre of Fuwai Hospital from 2012 to 2014 were primarily enrolled when blood samples for genetic testing were obtained within 24 hours of admission. Another 568 healthy control samples were obtained from individuals undergoing physical examination. The diagnosis of STAAD was confirmed using multidetector computed tomography scanning. All of the included patients reported the absence of a first-degree relative with aortic aneurysm or dissection in a detailed medical history inquiry. Patients with AD secondary to surgery, trauma and pregnancy were excluded. Baseline characteristic data were recorded, including sex, age, and previous medical histories, such as hypertension, diabetes mellitus, coronary artery disease, smoking status and drinking status. Other recorded clinical characteristics included baseline vital signs at admission (systolic/diastolic blood pressure and heart rate), imaging examinations and hospital management (medical therapy or surgical intervention). An experienced surgeon-in-charge determined the rationale and strategy of the surgical techniques according to the guidelines for the diagnosis and treatment of aortic disease (8,9). The primary end point was in-hospital all-cause mortality, and the evaluation of mortality was obtained from our hospital’s medical database. The study was performed in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of Fuwai Hospital (No. 2012-396). Informed consent was obtained from all patients.

WGS and variant calling method

Genomic DNA was isolated from blood samples. Novogene performed WGS. DNA libraries were sequenced on an Illumina HiSeq X according to the manufacturer’s instructions to generate paired-end 150 bp reads, and the researchers were blinded to phenotypic labels during the WGS process. Primer sequences were trimmed from FASTQ files using cutadapt (v 1.9.1) 20 prior to read mapping to the reference genome (UCSC hg19) using BWA-MEM. SAMtools (version 1.0) was used for variant calling and the identification of single-nucleotide variants (SNVs) and indels. Only variants with QualByDepth (QUAL) >20, Depth (DP) >4, and RMS MappingQuality (MQ) >40 passed the filter.

Mutation analysis

WGS was performed to scan for genetic variants that may underlie STAAD. This study also analysed 26 specific previously known genes (with exact OMIM numbers) that underlie aortic aneurysm and dissection. The detailed panel of tested genes is presented in . Polymorphic variants were excluded if their allelic frequency was >0.01 in the 1000 Genomes Project (in all populations, http://www.internationalgenome.org/1000-genomes-browsers) or in the 568 healthy controls. Variants were considered pathological if they met one of the following criteria: (I) previously reported as pathological in the NCBI ClinVar database; (II) nonsense and indel (frameshift or nonframeshift) mutations; (III) novel missense mutations that indicated a damaging effect in SIFT20 (http://sift.jcvi.org/) or PolyPhen-219 (http://genetics.bwh.harvard.edu/pph2/); and (IV) variations in the splice site within 3 bp of the exon.

Table 1

Panel of the 26 tested genes

No.	Type	Gene	OMIM No.	Clinical manifestation
1	ECM proteins	FBN1	154700	Marfan’s syndrome
2	ECM proteins	FBN2	612570	Beals syndrome; Contractural arachnodactyly
3	ECM proteins	MFAP5	616166	Aortic aneurysm, familial thoracic 9
4	ECM proteins	COL1A1	130000	Ehlers-Danlos syndrome, classic type
5	ECM proteins	COL1A2	130060	Ehlers-Danlos syndrome, procollagen proteinase deficient
6	ECM proteins	COL3A1	130050	Ehlers-Danlos syndrome, vascular type
7	ECM proteins	COL5A1	130000	Ehlers-Danlos syndrome, classic type
8	ECM proteins	COL5A2	130000	Ehlers-Danlos syndrome, classic type
9	ECM proteins	ADAMTS2	225041	Ehlers-Danlos syndrome type 7
10	ECM proteins	ADAMTS10	277600	Weill-Marchesani syndrome 1
11	ECM proteins	PLOD1	225400	Ehlers-Danlos syndrome, hydroxylysine-deficient
12	ECM proteins	PLOD3	612394	Bone fragility with contractures, arterial rupture, and deafness
13	ECM proteins	ELN	123700	Williams syndrome, Supravalvar aortic stenosis
14	ECM proteins	EFEMP2	614437	Cutis laxa autosomal recessive IIA
15	TGF-β pathway	TGFBR1	609192	Loeys-Dietz syndrome 1
16	TGF-β pathway	TGFBR2	190182	Loeys-Dietz syndrome 2
17	TGF-β pathway	SMAD3	613795	Loeys-Dietz syndrome 3
18	TGF-β pathway	TGFB2	190220	Loeys-Dietz syndrome 4
19	TGF-β pathway	TGFB3	615582	Loeys-Dietz syndrome 5
20	Cytoskeletal/smooth muscle contraction apparatus proteins	MYH11	132900	Aortic aneurysm, familial thoracic 4
21	Cytoskeletal/smooth muscle contraction apparatus proteins	ACTA2	611788	Aortic aneurysm, familial thoracic 6
22	Cytoskeletal/smooth muscle contraction apparatus proteins	MYLK	613780	Aortic aneurysm, familial thoracic 7
23	Cytoskeletal/smooth muscle contraction apparatus proteins	PRKG1	615436	Aortic aneurysm, familial thoracic 8
24	Cytoskeletal/smooth muscle contraction apparatus proteins	FLNA	300375	Heterotopia, periventricular, Ehlers-Danlos variant
25	Neural crest migration	NOTCH1	109730	Familial thoracic aortic aneurysm with bicuspid aortic valve
26	Facilitative glucose transporter	SLC2A10	208050	Arterial tortuosity syndrome

ECM, extracellular matrix; TGF, transforming growth factor.

Statistical analysis

All statistical analyses were performed using SPSS version 19.0 (SPSS, Inc., Chicago, Illinois, USA). Continuous variables are presented as the means ± SD or the medians and interquartile range-based Gaussian distribution. Baseline characteristics were compared between groups using unpaired Student’s t tests or chi-square tests. The in-hospital mortality was compared between the different groups using chi-square tests. A P value of <0.05 was considered statistically significant.

Results

Patient clinical characteristics

In total, 104 sporadic subjects who were diagnosed with STAAD were primarily enrolled, and WGS was performed in 96.1% (100/104) of patients with higher-quality blood DNA samples. The summarized clinical characteristics are shown in . The average age of these patients was 52.7±12.3 years, and 62.0% (62/100) of the enrolled patients were male. Eighty-four cases showed the DeBakey I AD phenotype, 16 cases were DeBakey type II, and 65 patients (65.0%) had a history of hypertension.

Table 2

Clinical characteristics of tested patients with Stanford type A AAD

Clinical characteristics	Total (n=100)
Age at onset, years	52.7±12.3
Male, n (%)	62 (62.0)
Family history, n (%)	0 (0.0)
Phenotype
DeBakey type I, n (%)	84 (84.0)
Comorbidities and risk factors
Hypertension, n (%)	65 (65.0)
Diabetes mellitus, n (%)	4 (4.0)
Coronary artery disease, n (%)	4 (4.0)
Smoke, n (%)	34 (34.0)
Alcohol history, n (%)	13 (13.0)
Clinical features
BMI, kg/m2	25.2±3.7
Ascending aorta diameter, mm	46.4±12.1
Facial features	0 (0.0)
Skeletal abnormalities	0 (0.0)

BMI, body mass index.

Technical performance: coverage and variant calling

After the entire run was completed, on average, 89.79 GB of Illumina sequencing data per subject were generated. The average sequencing depth of WGS was 32.58±2.59×, with an average coverage of 99.39% of the genome. The average depth of the analysed target genes of all samples was over 20×, and the coverage of the analysed genes in a specific sample was over 99.38% (Table S1). The percentage of each gene sequence covered using this assay is presented in Table S2. A summary of the quality control for WGS is presented in Table S3.

Genetic variants of panel genes for WGS

Site-based data

In total, 130 mutations were screened in the tested genes from the panel, including 5 indel mutations and 125 SNV mutations. Seventy mutations were excluded as common polymorphisms or neutral rare variants according to the exclusion criteria. After exclusion, 60 mutations, including 5 indels and 55 SNVs in 19 panel genes, were identified as disease-associated mutations (), and the allelic frequency of the identified variants in healthy controls is detailed in Table S4. Fifty-six mutations were novel, and 4 mutations were previously reported. Mutations located in the extracellular matrix (ECM)-coding genes, especially in FBN1 (10 mutations, 16.7%) and COL5A1 (6 mutations, 10.0%), constituted 61.7% (37/60) of these variants. Seventeen mutations (28.3%) were found in cytoskeletal or smooth muscle contraction apparatus protein-coding genes, and 5 mutations were found in coding genes in the transforming growth factor-β (TGF-β) pathway (2 in TGFBR1, 2 in TGFBR2 and 1 in TGFB3). Only 1 mutation was identified in the NOTCH1 gene, which was related to neural crest migration. Detailed site-based information is shown in .

Figure 1

Summary of the genetic variants of the panel genes based on the results of whole-genome sequencing. STAAD, Stanford type A aortic dissection; SNV, single-nucleotide variants.

Table 3

Detailed list of identified variants in patients with mutations of panel genes

No.	Affected genes	Chromosome location (HG 19)	Transcription	Exon	Variant (DNA level)	Variant (protein level)	Variant type	Variant previously reported	Sift†	Polyphen‡	Patient ID	Age, sex
1	FBN1	Chr15:48703206	NM_000138	exon66	c.T8597A	p.I2866N	Missense	Novel	D	P	A486	59, F
	FBN1	Chr15:48717611	NM_000138	exon60	c.T7408G	p.C2470G	Missense	Novel	D	D	A403	43, F
	FBN1	Chr15:48720626	NM_000138	exon57	c.G6914C	p.G2305A	Missense	Novel	T	D	A65	55, M
	FBN1	Chr15:48737635	NM_000138	exon48	c.G5855A	p.G1952E	Missense	Novel	D	D	A483	51, M
	FBN1	Chr15:48766500	NM_000138	exon34	c.C4162T	p.R1388C	Missense	Novel	D	P	A73	45, M
	FBN1	Chr15:48787358	NM_000138	exon22	c.G2639A	p.G880D	Missense	Novel	D	D	A342	48, M
	FBN1	Chr15:48787384	NM_000138	exon22	c.A2613C	p.L871F	Missense	Novel	D	D	A291	20, F
	FBN1	Chr15:48812996	NM_000138	exon10	c.G1007C	p.C336S	Missense	Novel	T	D	A458	42, M
	FBN1	Chr15:48738912	NM_000138	exon47	c.5778delT	p.N1926fs	Frameshift deletion	Novel	D	D	A203	49, M
	FBN1	Chr15:48802262	NM_000138	exon14	c.C1693T	p.R565X	Nonesense	Known	D	D	A434	35, M
2	MYH11	Chr16:15814118	NM_002474	exon34	c.G4843A	p.A1615T	Missense	Novel	D	P	A437	60, M
	MYH11	Chr16:15820797	NM_002474	exon28	c.A3766C	p.K1256Q	Missense	Novel	D	D	A445; A485	51,F; 81,F
	MYH11	Chr16:15814752	NM_002474	exon33	c.G4735A	p.D1579N	Missense	Novel	D	D	A295	76, M
	MYH11	Chr16:15814883	NM_002474	exon33	c.G4604A	p.R1535Q	Missense	Novel	D	D	A486; A263	59, F; 46, M
	MYH11	Chr16:15815415	NM_002474	exon32	c.A4442T	p.K1481M	Missense	Novel	T	D	A313	72, F
	MYH11	Chr16:15931842	NM_001040113	exon2	c.A268G	p.M90V	Missense	Novel	D	D	A483	51, M
	MYH11	Chr16:15820794	NM_002474	exon28	c.3757_3759del	p.1253_1253del	Nonframeshift deletion	Novel	D	D	A315	67, M
3	MYLK	Chr3:123356997	NM_053026	exon28	c.G4675A	p.V1559M	Missense	Novel	D	D	A242	55, M
	MYLK	Chr3:123419455	NM_053026	exon17	c.C2653T	p.R885C	Missense	Novel	T	D	A137	70, M
	MYLK	Chr3:123427731	NM_053026	exon14	c.C1747G	p.P583A	Missense	Novel	T	D	A199	30, F
	MYLK	Chr3:123427662	NM_053026	exon14	c.G1816A	p.G606R	Missense	Novel	D	D	A197; A260	38, M; 77, F
	MYLK	Chr3:123337586	NM_053031	exon2	c.113_114insTG	p.A38fs	Frameshift insertion	Novel	D	D	A480	52, F
	MYLK	Chr3:123452658	NM_053025	exon10	c.1179_1181del	p.393_394del	Nonframeshift deletion	Novel	D	D	A209	49, F
4	COL5A1	Chr9:137582848	NM_000093	exon2	c.C200T	p.S67F	Missense	Novel	D	P	A459	44, M
	COL5A1	Chr9:137591878	NM_000093	exon3	c.G401A	p.R134H	Missense	Novel	D	D	A185	46, F
	COL5A1	Chr9:137623972	NM_000093	exon9	c.C1388T	p.P463L	Missense	Novel	D	D	A436	66, M
	COL5A1	Chr9:137698140	NM_000093	exon42	c.C3364A	p.P1122T	Missense	Novel	D	B	A458	42, M
	COL5A1	Chr9:137701090	NM_000093	exon43	c.C3428T	p.P1143L	Missense	Novel	T	D	A309	38, M
	COL5A1	Chr9:137727015	NM_000093	exon65	c.A5335G	p.N1779D	Missense	Novel	D	B	A282	54, F
5	ELN	Chr7:73474880	NM_001278939	exon26	c.G1883C	p.G628A	Missense	Novel	D	–	A199	30, F
	ELN	Chr7:73470666	NM_001278913	exon17	c.G1108A	p.G370S	Missense	Novel	T	D	A295	76, M
	ELN	Chr7:73466278	NM_001278913	exon14	c.C806T	p.A269V	Missense	Novel	D	D	A95	43, M
	ELN	Chr7:73461035	NM_001278918	exon9	c.C449T	p.P150L	Missense	Novel	T	D	A201	75, F
	ELN	Chr7:73449715	NM_000501	exon2	c.G104C	p.G35A	Missense	Novel	T	D	A239	66, F
6	ACTA2	Chr10:90701550	NM_001141945	exon5	c.G446A	p.R149H	Missense	Known	D	D	A130; A406	42, M; 64, F
	ACTA2	Chr10:90699437	NM_001141945	exon7	c.G635A	p.R212Q	Missense	Known	D	D	A199; A476	30, F; 43, F
	ACTA2	Chr10:90707140	NM_001141945	exon3	c.G133T	p.V45L	Missense	Novel	D	D	A451	76, F
7	COL1A2	Chr7:94055131	NM_000089	exon44	c.G2905A	p.V969M	Missense	Novel	D	B	A349	57, M
	COL1A2	Chr7:94052321	NM_000089	exon40	c.G2456A	p.R819H	Missense	Novel	D	D	A199; A291	30, F 20, F
8	FBN2	Chr5:127714544	NM_001999	exon12	c.A1643C	p.D548A	Missense	Novel	T	D	A282	54, F
	FBN2	Chr5:127800434	NM_001999	exon6	c.G809T	p.R270L	Missense	Novel	D	D	A246	60, M
	FBN2	Chr5:127670946	NM_001999	exon30	c.G3889A	p.G1297S	Missense	Novel	D	D	A457	43, M
9	PLOD3	Chr7:100854915	NM_001084	exon12	c.G1315A	p.A439T	Missense	Novel	D	P	A320	40, M
	PLOD3	Chr7:100850890	NM_001084	exon17	c.C1904T	p.T635I	Missense	Novel	T	D	A58	68, F
	PLOD3	Chr7:100858379	NM_001084	exon6	c.G670A	p.G224R	Missense	Novel	D	D	A180	63, F
10	COL1A1	Chr17:48267260	NM_000088	exon37	c.C2573G	p.A858G	Missense	Novel	D	B	A349	57, M
	COL1A1	Chr17:48269364	NM_000088	exon30	c.G2005A	p.A669T	Missense	Novel	T	D	A65	55. M
11	COL3A1	Chr2:189870953	NM_000090	exon42	c.C3061A	p.L1021I	Missense	Novel	T	D	A180	63, F
	COL3A1	Chr2:189859447	NM_000090	exon20	c.1348-3C>T	–	Splicing site	Novel	–	–	A480	52, F
12	EFEMP2	Chr11:65635400	NM_016938	exon10	c.G1102A	p.V368I	Missense	Novel	T	D	A131	58, F
	EFEMP2	Chr11:65638012	NM_016938	exon5	c.G485A	p.C162Y	Missense	Novel	D	D	A342	48, M
13	TGFBR1	Chr9:101904938	NM_001130916	exon4	c.C695T	p.T232M	Missense	Novel	D	D	A217	38, M
	TGFBR1	Chr9:101911496	NM_001130916	exon8	c.G1190A	p.C397Y	Missense	Novel	D	D	A64	55, F
14	TGFBR2	Chr3:30732951	NM_003242	exon7	c.G1564A	p.D522N	Missense	Known	D	D	A290	31, F
	TGFBR2	Chr3:30713543	NM_003243	exon4	c.871_873del	p.291_291del	Nonframeshift deletion	Novel	D	D	A291	20, F
15	COL5A2	Chr2:189918632	NM_000393	exon37	c.C2488T	p.R830W	Missense	Novel	–	D	A163	49, M
16	FLNA	ChrX:153590106	NM_001110556	exon20	c.G2876A	p.S959N	Missense	Novel	D	D	A448	53, F
17	NOTCH1	Chr9:139393702	NM_017617	exon32	c.C5944T	p.R1982W	Missense	Novel	D	D	A457	43, M
18	PLOD1	Chr1:12017040	NM_000302	exon7	c.C710T	p.P237L	Missense	Novel	D	D	A349	57, M
19	TGFB3	Chr14:76427339	NM_003239	exon6	c.C1007T	p.P336L	Missense	Novel	D	D	A246	60, M

†, SIFT prediction: D, not tolerated; T, tolerated; ‡, PolyPhen prediction: D, probably damaging; P, possibly damaging; B, benign. –, not applicable.

Case-based data

We identified genetic mutations in half of the patients (50/100) using the gene panel for aortic aneurysm and dissection. The other half of the patients showed no deleterious mutations. From the data, more than 1 variant was found in 14 patients (1 patient had 4 mutations, 2 patients had 3 mutations, and 11 patients had 2 mutations), and the other 36 patients carried only 1 mutation ().

Figure 2

Gene mutations identified by whole-genome sequencing in 100 subjects with sporadic Stanford type A aortic dissection. Fourteen percent of patients (14/100) carried more than one mutation.

Genetic mutations and clinical features and in-hospital death

Baseline clinical feature

Patients were grouped according to the presence of pathogenic variants [with (n=50) or without mutations of the panel genes (n=50)]. Patients with mutations were subdivided into a single-mutation group (n=36) or multiple-mutation group (n=14) according to the number of variants. Patients with mutations were further subdivided into groups based on whether they had mutations in the ECM coding gene (n=29) or only carried mutations in other genes (n=21). Comparisons of clinical characteristics in patients with respect to the presence and type of pathogenic mutations are detailed in .

Table 4

Baseline characteristics of sporadic subjects diagnosed with Stanford type A AAD related to the presence, number and type of pathogenic mutations

Clinical characteristics	Total (n=100)	Mutations of the panel genes				Number of mutations of the panel genes				Type of mutations
		With (n=50)	Without (n=50)	P value		Single (n=36)	Multiple (n=14)	P value		ECM coding (n=29)	Other (n=21)	P value
Age at onset, years	52.7±12.3	52.7±13.8	52.6±10.7	0.948		53.5±13.8	50.7±13.9	0.522		50.7±12.5	55.5±15.3	0.228
Male, n (%)	62 (62.0)	29 (58.0)	33 (66.0)	0.410		20 (55.6)	9 (64.3)	0.547		20 (69.0)	9 (42.9)	0.086
Height, cm	169.1±7.7	168.5±7.8	169.7±7.6	0.455		167.9±7.9	170.1±7.8	0.382		170.1±7	166.2±8.6	0.083
Weight, kg	71.9±12.3	71.5±12.5	72.3±12.2	0.737		71.8±11.8	70.9±14.4	0.836		74±13.1	68.1±10.8	0.102
BMI, kg/m2	25.2±3.7	25.1±3.8	25.1±3.5	0.915		25.4±3.6	24.4±4.5	0.426		25.5±4	24.7±3.7	0.449
DeBakey type I, n (%)	84 (84.0)	42 (84.0)	42 (84.0)	1.000		10 (88.9)	10 (71.4)	0.197		28 (96.6)	14 (66.7)	0.007
Hypertension, n (%)	65 (65.0)	33 (66.0)	32 (64.0)	0.834		24 (66.7)	9 (64.3)	1.000		18 (62.1)	15 (71.4)	0.490
Diabetes mellitus, n (%)	4 (4.0)	0 (0.0)	4 (8.0)	0.126		0 (0.0)	0 (0.0)	1.000		0 (0.0)	0 (0.0)	1.000
Coronary artery disease, n (%)	4 (4.0)	2 (4.0)	2 (4.0)	1.000		0 (0.0)	2 (14.3)	0.131		2 (6.9)	0 (0.0)	0.503
Smoke, n (%)	34 (34.0)	17 (34.0)	17 (34.0)	1.000		12 (33.3)	5 (35.7)	1.000		14 (48.3)	6 (28.6)	0.160
Alcohol history, n (%)	13 (13.0)	6 (12.0)	7 (14.0)	1.000		4 (11.1)	2 (14.3)	1.000		4 (13.8)	4 (19.0)	0.706
SBP, mmHg	119.7±18.9	119.6±18.2	119.8±19.8	0.962		118±18.9	123.6±16.6	0.310		118.9±20.4	120.6±15.2	0.741
DBP, mmHg	62.9±12.2	63.2±13.2	62.6±11.2	0.794		61.4±13.3	67.9±12.1	0.110		62.2±12.3	64.6±14.5	0.537
Heart rate, bpm	84.8±11.9	84.1±12.2	85.6±11.6	0.525		83.3±13.2	86.1±9.2	0.458		86.1±14	81.3±8.7	0.175
Ascending aorta diameter, mm	46.4±12.1	49.1±12.3	43.7±11.2	0.023		49.9±12.7	47.1±11.5	0.465		49.3±11.9	48.9±13.2	0.904
Surgical intervention, n (%)	63 (63.0)	32 (64.0)	31 (62.0)	0.836		22 (61.6)	10 (71.4)	0.495		21 (72.4)	11 (52.4)	0.145

ECM, extracellular matrix; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

An extended ascending aortic diameter was found in patients with mutations in the panel genes (49.1±12.3 vs. 43.7±11.2 mm, P=0.023) compared to patients without mutations. The DeBakey type I phenotype was more common in patients with mutations in ECM coding genes than in patients with mutations in other genes (96.6% vs. 66.7%, P=0.007). Other clinical features were comparable between the groups (all Ps >0.05). Table S5 shows the clinical characteristics of patients with the top 3 most frequent gene mutations (FBN1, MYH11, MYLK) in this cohort. FBN1 was the most frequently mutated gene (n=10), and the onset age differed significantly between the three genes (P=0.021). The onset age of patients with mutations in FBN1 was younger than that of patients without FBN1 mutations (44.7±11.0 vs. 53.5±12.1, P=0.030).

In-hospital outcome

The overall in-hospital mortality was 12.0% (12/100) in all enrolled patients with STAAD. Of these patients, 66.6% (8/12) died of aortic rupture, and 33.3% (4/12) died of cardiac issues (Table S6). Clinical outcomes in these patients were summarized according to the presence, number and type of mutations (). In-hospital mortality was 3-fold higher in patients with mutations than in patients without mutations, but the difference was not statistically significant (18.0% vs. 6.0%, P=0.065). The in-hospital death rate was comparable regardless of the number or type of mutation the patients carried (all Ps >0.05, ).

Figure 3

In-hospital outcome in subjects with Stanford type A aortic dissection. (A) Comparison of in-hospital mortality according to the presence, number and type of mutations. Subgrouped comparisons of all-cause mortality stratified by the presence (B), number (C) and type (D) of mutations in patients who received conservative treatment or surgical intervention.

Because of the significant impact of surgical treatment on in-hospital death from STAAD, patients were subdivided into a conservative treatment group and a surgical intervention group. The in-hospital mortality was 27.0% (10/37) and 3.2% (2/63) in patients with conservative and surgical treatments, respectively. Subgroup analysis of in-hospital mortality was also performed in different treatment groups according to the presence, number and type of mutations. shows that in-hospital mortality was comparable between patients with and without mutations who received surgical treatment (3.1% vs. 3.2%, P=1.000). However, increased in-hospital mortality in mutation carriers was observed only in patients who received conservative treatment (44.4% vs. 10.5%, P=0.029). When the in-hospital mortality was compared between the mutation number and mutations in different gene groups, no statistically significant differences were observed (all Ps >0.05, in ).

Discussion

The current study analysed genetic variants of 26 panel genes that cause thoracic aortic aneurysm and aortic dissection (TAAD) in 100 patients with sporadic STAAD using WGS. Sixty probable disease-causing mutations were identified in half of the enrolled patients for 19 of the 26 panel genes. Fifty-six of these mutations were newly discovered, and 4 mutations were previously reported. Patients with mutations in the panel genes had an ascending aorta with a larger diameter and a poorer in-hospital outcome than patients without mutations. Patients with mutations in ECM coding genes seemed more likely to develop a severe AD phenotype (DeBakey type I) than patients with mutations in other genes.

Previous studies have shown that genetic disorders are associated with aortic aneurysm and dissection. NGS-based gene panel testing is widely used to detect genetic susceptibility to aortic disorders. Three studies investigated the genetic variants in TAAD in European populations. Poninska et al. used whole-exome sequencing (WES) and a gene panel to study 51 patients with TAAD and reported a 35.3% diagnostic yield (10). Campens et al. found variants in 13% of TAAD patients using a panel of 7 genes (11). Proost et al. screened 14 genes from 55 patients and identified 15 pathogenic mutations and six variants of uncertain significance (12). An American study with a larger sample size (n=102) showed that 4.9% of patients carried a pathogenic/likely pathogenic variant, and 22% had a variant of uncertain significance based on a 21-gene panel (13). Wooderchak et al. found pathogenic variants in 10% of patients and variants of uncertain significance in 18% of patients (14). Zheng et al. found genotype-positive variants in 28.8% of TAAD patients in a South Chinese Han cohort using a 69-gene panel (15), and Fang et al. identified 40 variants (3 pathogenic, 10 likely pathogenic and 27 variants of uncertain significance) in 36 of 70 TAAD patients (16). Similar to the two Chinese investigations, the genotype-positive rate in our study was clearly higher than that in Western cohorts. The higher mutation rate may be due to the following reasons. First, we only included patients without a family history, and the genetic changes in sporadic patients may differ from those in patients with a family history. These patients may have a greater likelihood of carrying a single mutation with a relatively lower penetrance and show a sporadic form. Second, we only included patients with STAAD who had a stronger genetic component than patients with Stanford type B AD or patients with aortic aneurysms who showed associations with lifestyle-linked risk factors. Third, the patients included in this study were primarily northern Han Chinese people, whose genetic backgrounds are completely different and who have a younger onset age of AD compared to Western populations.

MFS is the most common aortic aneurysm syndrome caused by heterozygous mutations in FBN1. A genome-wide association study (GWAS) analysed 765 sporadic cases of TAAD, including STAAD, and found that common single-nucleotide polymorphisms (SNPs) in the FBN1 gene were significantly associated with an increased risk of developing aortic aneurysms and dissections (17). Rare mutations of FBN1 were also found in 15.75% of STAAD cases in a Chinese population (18). Mutations in FBN1 were also the most frequent (10 mutations, 1 known and 9 novel) in sporadic STAAD in our study. However, except for a previously reported stop-gain mutation (exon 14, pR565X) (19), none of these mutations were located in the central coding sequences (exons 24–32) of the FBN1 gene, the disturbance of which may cause a severe phenotype of MFS (20,21). The location of these variants may partially explain the milder penetrance and variable expressivity in the sporadic form. Consistent with studies that reported on Marfan and non-Marfan patients with aortic disorders (22,23), patients carrying mutations in FBN1 were younger than other patients in our study.

Collagens are the most important components of the ECM, and certain coding genes in collagens are related to EDS. The syndrome, which causes a disorder in the connective tissue, is divided into different phenotypes based on changes in different genes (24,25). AD is one of the most severe complications of this syndrome, especially in EDS type IV (also known as vascular type, with mutations in COL3A1) (26). Patients with EDS show poor prognosis due to the fragility of aortic tissues and poor wound healing. According to data from a previous study (27) and our study, a considerable proportion of sporadic cases of AD showed likely pathogenic variants in EDS-related genes, which may partially explain the poor outcome in some of the AD patients. Other genetic variants were also found in genes responsible for LDS (mutations related to the TGF-β signalling pathway) at a relatively lower ratio (5% cases). De novo mutations were found in 75% of patients with LDS (28), and LDS can occur in sporadic form (29). Five probable pathogenic variants were also found in LDS-related genes in our cohort, including TGFBR1, TGFBR2 and TGFB3.

The ECM is the key structural component of the aorta, as ECM elements provide elasticity and tensile strength to blood vessel walls. The ECM also provides important growth factors, such as TGF-β (6). In our study, the DeBakey type I phenotype was more common in patients with mutations in ECM coding genes (96.6%), which indicates a larger extent of damage. The findings indicated that defects in the ECM components likely had a destructive impact on the structure of the aorta and showed a distinct relationship with the clinical phenotype.

Genetic variants related to vascular smooth muscle cell (SMC) contractility often show associations with nonsyndromic aortic aneurysms and dissections, such as actin alpha 2 (ACTA2), MYH11 and myosin light-chain kinase (MYLK). The ACTA2 gene encodes an SMC-specific isoform of the contractile protein α-actin (30-32). Mutations in ACTA2 are the most common reason for nonsyndromic aortic aneurysms and dissection and account for approximately 2–4% of sporadic cases of aortic disorder (7,32). Similar to previously reported results, we found that 5% (5/100) of patients carried 3 mutations in ACTA2. Two of these mutations were previously reported (33) but corresponded to different changes (p. R149H and p.R212Q). The number of mutations in MYLK and MYH11 was 13, affecting 16 patients, and all of these mutations were novel. Mutations in MYH11 are associated with familial TAAD (5), but the average age of onset was difficult to judge in different familial forms. Nine sporadic patients who carried mutations in MYH11 in our study were older than the other patients. However, this observation may be due to the limited sample size, and further confirmation in a larger cohort is needed in future studies.

The clinical data of this study revealed that genetic differences showed an association with the phenotype of the individual and clinical outcomes. Variants that increase susceptibility to AD may also increase the risk of in-hospital death. This phenomenon stresses the importance of genetically personalized care and subsequent precision treatment in STAAD, especially patients with mild-to-moderate dilation of the ascending aorta, even without a family history. Although additional novel genes were associated with aortic diseases, routine genetic testing using classic gene panels showed strength in the identification of pathogenic variants in STAAD patients (13). A considerable proportion of STAAD patients were associated with mutations in known pathogenic genes. We also recommend that routine genetic testing be performed first in clinical practice, and other patients with AD may undergo WGS or WES to identify previously unreported changes.

The present study has two important strengths. First, the present study used an enlarged panel of classic pathogenic genes to investigate AD-related genetic changes in a Chinese population with a rare mutation analysis strategy and did not analyse only common polymorphisms. The rate of mutation carriers in our cohort was almost twice the rate of TAAD in a study performed in a Western population (10-14). This difference may partially explain the younger onset age of AD in Chinese patients (34). Second, the present study revealed the potential impact of certain genetic changes on the clinical features and outcomes of AD, which may be of great transitional significance in clinical management.

Several limitations of the present study must be mentioned. First, confounding factors may exist due to the single-centre setting, and the findings obtained in this cohort may not extend to other populations. The small sample size also did not allow for extended analyses of subgroups and corrections. Second, whether the probable pathogenic variants are disease-causing or benign must be determined. Finally, we only primarily reviewed part of the data from WGS, and further analyses and confirmations have not been completed. Despite the relatively small sample size, the clinical features and outcomes showed a relationship with the pathogenic genotype. However, the insufficient sample size restricted further analyses of clinical data, and confirmations from a large multicentre cohort may be needed in the future.

Conclusions

Our study indicated that half of Chinese patients with sporadic STAAD may carry mutations in known pathogenic genes of aortic disease and may exhibit severe clinical features and poor clinical outcomes with conservative treatment.

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